scholarly journals An Admissible HTN Planning Heuristic

2017 ◽  
Author(s):  
Pascal Bercher ◽  
Gregor Behnke ◽  
Daniel Höller ◽  
Susanne Biundo
Keyword(s):  
Search Space ◽  
Planning System ◽  
Hierarchical Task Network ◽  
Planning Approach ◽  
Htn Planning ◽  
Efficient Planning ◽  
Being There ◽  
Control Knowledge ◽  
Search Control ◽  
Domain Independent

Hierarchical task network (HTN) planning is well-known for being an efficient planning approach. This is mainly due to the success of the HTN planning system SHOP2. However, its performance depends on hand-designed search control knowledge. At the time being, there are only very few domain-independent heuristics, which are designed for differing hierarchical planning formalisms. Here, we propose an admissible heuristic for standard HTN planning, which allows to find optimal solutions heuristically. It bases upon the so-called task decomposition graph (TDG), a data structure reflecting reachable parts of the task hierarchy. We show (both in theory and empirically) that rebuilding it during planning can improve heuristic accuracy thereby decreasing the explored search space. The evaluation further studies the heuristic both in terms of plan quality and coverage.

scholarly journals Decision-Theoretic Planning with non-Markovian Rewards

2006 ◽  
Vol 25 ◽  
pp. 17-74 ◽  
Author(s):  
S. Thiebaux ◽  
C. Gretton ◽  
J. Slaney ◽  
D. Price ◽  
F. Kabanza
Keyword(s):  
Decision Process ◽  
Single Interface ◽  
Reward Function ◽  
Domain Specific ◽  
Current State ◽  
Markovian Decision Process ◽  
Solution Methods ◽  
Control Knowledge ◽  
Search Control ◽  
Domain Independent

A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decision-theoretic planning, where many desirable behaviours are more naturally expressed as properties of execution sequences rather than as properties of states, NMRDPs form a more natural model than the commonly adopted fully Markovian decision process (MDP) model. While the more tractable solution methods developed for MDPs do not directly apply in the presence of non-Markovian rewards, a number of solution methods for NMRDPs have been proposed in the literature. These all exploit a compact specification of the non-Markovian reward function in temporal logic, to automatically translate the NMRDP into an equivalent MDP which is solved using efficient MDP solution methods. This paper presents NMRDPP (Non-Markovian Reward Decision Process Planner), a software platform for the development and experimentation of methods for decision-theoretic planning with non-Markovian rewards. The current version of NMRDPP implements, under a single interface, a family of methods based on existing as well as new approaches which we describe in detail. These include dynamic programming, heuristic search, and structured methods. Using NMRDPP, we compare the methods and identify certain problem features that affect their performance. NMRDPP's treatment of non-Markovian rewards is inspired by the treatment of domain-specific search control knowledge in the TLPlan planner, which it incorporates as a special case. In the First International Probabilistic Planning Competition, NMRDPP was able to compete and perform well in both the domain-independent and hand-coded tracks, using search control knowledge in the latter.

Feature-Based Machining Precedence Reasoning and Sequence Planning

1998 ◽  
Author(s):  
Yong Se Kim ◽  
Eric Wang ◽  
Choong Soo Lee ◽  
Hyung Min Rho
Keyword(s):  
Feature Recognition ◽  
Search Space ◽  
Machining Process ◽  
Planning System ◽  
Process Information ◽  
Computer Aided Process Planning ◽  
Sequence Planning ◽  
Precedence Relations ◽  
Feature Based ◽  
Form Features

Abstract This paper presents a feature-based method to support machining sequence planning. Precedence relations among machining operations are systematically generated based on geometric information, tolerance specifications, and machining expertise. The feature recognition method using Alternating Sum of Volumes With Partitioning (ASVP) Decomposition is applied to obtain a Form Feature Decomposition (FFD) of a part model. Form features are classified into a taxonomy of atomic machining features, to which machining process information has been associated. Geometry-based precedence relations between features are systematically generated using the face dependency information obtained by ASVP Decomposition and the features’ associated machining process information. Multiple sets of precedence relations are generated as alternative precedence trees, based on the feature types and machining process considerations. These precedence trees are further enhanced with precedence relations from tolerance specifications and machining expertise. Machining sequence planning is performed for each of these precedence trees, applying a matrix-based method to reduce the search space while minimizing the number of tool changes. The precedence trees may then be evaluated based on machining cost and other criteria. The precedence reasoning module and operation sequence planning module are currently being implemented within a comprehensive Computer-Aided Process Planning system.

scholarly journals HDDL: An Extension to PDDL for Expressing Hierarchical Planning Problems

2020 ◽  
Vol 34 (06) ◽  
pp. 9883-9891 ◽  
Author(s):  
Daniel Höller ◽  
Gregor Behnke ◽  
Pascal Bercher ◽  
Susanne Biundo ◽  
Humbert Fiorino ◽  
...  
Keyword(s):  
Planning System ◽  
Hierarchical Planning ◽  
Planning Systems ◽  
Description Language ◽  
Input Language ◽  
Common Input ◽  
Modeling Process ◽  
Planning Problems ◽  
Domain Independent ◽  
Tailored Advice

The research in hierarchical planning has made considerable progress in the last few years. Many recent systems do not rely on hand-tailored advice anymore to find solutions, but are supposed to be domain-independent systems that come with sophisticated solving techniques. In principle, this development would make the comparison between systems easier (because the domains are not tailored to a single system anymore) and – much more important – also the integration into other systems, because the modeling process is less tedious (due to the lack of advice) and there is no (or less) commitment to a certain planning system the model is created for. However, these advantages are destroyed by the lack of a common input language and feature set supported by the different systems. In this paper, we propose an extension to PDDL, the description language used in non-hierarchical planning, to the needs of hierarchical planning systems.

scholarly journals Integrating strategic planning values into urban master planning process in Vietnam

2018 ◽  
Vol 193 ◽  
pp. 04017
Author(s):  
Ma Van Phuc ◽  
Tran Trung Vinh
Keyword(s):  
Strategic Planning ◽  
Planning Process ◽  
Planning System ◽  
Local Context ◽  
Master Planning ◽  
Planning Approach ◽  
Advance Method ◽  
Modern Context ◽  
And Control ◽  
Rapid Transformation

Today, Urban Planning in Vietnam faces a lot of problems due to the paradox between traditional master planning systems and modern context of rapid transformation. Alternatives are practiced in some cities, especially in the context of international integration. Various methods and cooperative projects have attempted to subvert the strict master-planning approach with high levels of centralization and reliance upon planning by command and control. However, despite many efforts, it seems impossible to completely replace current planning system by another advance method, which has no attachments with its developed context. The paper studies and schematizes the complexity of master planning process in Vietnam, which is regulated by various legal documentaries. On the other hand, it simultaneously studies strategic planning method and selectively chooses valuable features which fits local context. By confronting and integrating them with each other, the paper aims to introduce an advance master planning process that adaptive and flexible to modern challenges.

scholarly journals A Sufficient Condition for Convex Hull Property in General Convex Spatio-Temporal Corridors

2021 ◽  
Author(s):  
Weize Zhang ◽  
Peyman Yadmellat ◽  
Zhiwei Gao
Keyword(s):  
Convex Hull ◽  
Motion Planning ◽  
Autonomous Driving ◽  
Search Space ◽  
Sufficient Condition ◽  
Simple Shape ◽  
Planning Approach ◽  
General Convex ◽  
Simulation Results ◽  
Spatio Temporal

Motion planning is one of the key modules in autonomous driving systems to generate trajectories for self-driving vehicles to follow. A common motion planning approach is to generate trajectories within semantic safe corridors. The trajectories are generated by optimizing parametric curves (e.g. Bezier curves) according to an objective function. To guarantee safety, the curves are required to satisfy the convex hull property, and be contained within the safety corridors. The convex hull property however does not necessary hold for time-dependent corridors, and depends on the shape of corridors. The existing approaches only support simple shape corridors, which is restrictive in real-world, complex scenarios. In this paper, we provide a sufficient condition for general convex, spatio-temporal corridors with theoretical proof of guaranteed convex hull property. The theorem allows for using more complicated shapes to generate spatio-temporal corridors and minimizing the uncovered search space to $O(\frac{1}{n^2})$ compared to $O(1)$ of trapezoidal corridors, which can improve the optimality of the solution. Simulation results show that using general convex corridors yields less harsh brakes, hence improving the overall smoothness of the resulting trajectories.

scholarly journals The GRT Planning System: Backward Heuristic Construction in Forward State-Space Planning

2001 ◽  
Vol 15 ◽  
pp. 115-161 ◽  
Author(s):  
I. Refanidis ◽  
I. Vlahavas
Keyword(s):  
State Space ◽  
Intermediate State ◽  
Forward Direction ◽  
Planning System ◽  
Search Process ◽  
Space Planning ◽  
Two Phases ◽  
Performance Results ◽  
Domain Independent ◽  
State Space Planning

This paper presents GRT, a domain-independent heuristic planning system for STRIPS worlds. GRT solves problems in two phases. In the pre-processing phase, it estimates the distance between each fact and the goals of the problem, in a backward direction. Then, in the search phase, these estimates are used in order to further estimate the distance between each intermediate state and the goals, guiding so the search process in a forward direction and on a best-first basis. The paper presents the benefits from the adoption of opposite directions between the preprocessing and the search phases, discusses some difficulties that arise in the pre-processing phase and introduces techniques to cope with them. Moreover, it presents several methods of improving the efficiency of the heuristic, by enriching the representation and by reducing the size of the problem. Finally, a method of overcoming local optimal states, based on domain axioms, is proposed. According to it, difficult problems are decomposed into easier sub-problems that have to be solved sequentially. The performance results from various domains, including those of the recent planning competitions, show that GRT is among the fastest planners.

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